ChatGPT has Aced the Test of Understanding in College Economics: Now What?
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The Test of Understanding in College Economics (TUCE) is a standardized test of economics knowledge performed in the United States which primarily targets principles-level understanding. We asked ChatGPT to complete the TUCE. ChatGPT ranked in the 91st percentile for Microeconomics and the 99th percentile for Macroeconomics when compared to students who take the TUCE exam at the end of their principles course. The results show that ChatGPT is capable of providing answers that exceed the mean responses of students across all institutions. The emergence of artificial intelligence presents a significant challenge to traditional assessment methods in higher education. An important implication of this finding is that educators will likely need to redesign their curriculum in at least one of the following three ways: reintroduce proctored, in-person assessments; augment learning with chatbots; and/or increase the prevalence of experiential learning projects that artificial intelligence struggles to replicate well.
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Generative AI has demonstrated human-level performance on a broad range of tasks.
“Generative AI, such as OpenAI’s ChatGPT, has rapidly emerged as a disruptive technology capable of achieving human-level performance on a broad range of tasks (1–5).”
From Generative AI without guardrails can harm learning: Evidence from high school mathematics (2025) · cites this paper as [4]
The Introduction (pp. 1–2) describes ChatGPT responses as often indistinguishable from human writing and reports above-average test performance across several academic fields. The Conclusion (p. 9) reports its high scores on college economics tests. These passages support human-level performance on academic assessments, but not the broader claim across a wide range of task types or generative AI systems generally. (LLM, conf=0.83)
Experimental — accuracy not yet measuredChatGPT scores in the 90th percentile on standardized exams.
“In economics education, Geerling et al. (2023) demonstrate that ChatGPT scores in the 90th percentile on standardized exams, showing how easily it can produce high-quality academic work without genuine student engagement.”
From Generative AI-Enhanced Oral Exams in Economics Education (2026) · cites this paper as Geerling et al. (2023)
The Conclusion (p. 9) reports that ChatGPT ranked at the 91st percentile in microeconomics and the 99th percentile in macroeconomics on the standardized TUCE exams. This supports the claim of high exam performance, but “the 90th percentile” obscures the distinct results, and the study does not establish performance on standardized exams generally. (LLM, conf=0.85)
Experimental — accuracy not yet measuredBuilding supportive and engaging learning environments that complement traditional pedagogical methods can provide a dynamic and interactive platform to foster knowledge acquisition and acknowledge existing concerns on plagiarism and academic integrity.
“An alternative to using GenAI in education, beyond exams and assignment writing, is building supportive and engaging learning environments that complement traditional pedagogical methods. This proposition can provide a dynamic and interactive platform to foster knowledge acquisition and acknowledge the existing concerns on plagiarism and academic integrity [8,16].”
From Using Generative Artificial Intelligence Tools to Explain and Enhance Experiential Learning for Authentic Assessment (2024) · cites this paper as [8]
ChatGPT can provide answers in exams that exceed the mean responses of students, which poses a significant challenge to traditional assessment methods in higher education.
“Some recent research results show that ChatGPT can provide answers in exams that exceed the mean responses of students, which poses a significant challenge to traditional assessment methods in HE [8].”
From Using Generative Artificial Intelligence Tools to Explain and Enhance Experiential Learning for Authentic Assessment (2024) · cites this paper as [8]
ChatGPT performs well in exams in disciplines such as engineering, economics, medicine, and geography.
“Related research shows that ChatGPT performs well in exams in disciplines such as engineering (Uhlig et al., 2023), economics (Geerling et al., 2023), medicine (Currie et al., 2023), and geography (Stutz et al., 2023), indicating that the issue of academic integrity in higher education is facing unprecedented challenges.”
From A Scoping Review on How Generative Artificial Intelligence Transforms Assessment in Higher Education (2024) · cites this paper as Geerling et al., 2023
ChatGPT offers opportunities for authentic assessment by challenging students' beliefs and critical thinking.
“Moreover, ChatGPT offers opportunities for authentic assessment by challenging students’ beliefs and critical thinking (Crawford et al., 2023; Currie et al., 2023; Geerling et al., 2023).”
From A Scoping Review on How Generative Artificial Intelligence Transforms Assessment in Higher Education (2024) · cites this paper as Geerling et al., 2023
Traditional examinations and assignments may become obsolete due to AI-generated content.
“as traditional examinations and assignments may become obsolete due to AI-generated content (Geerling et al., 2023; Gorichanaz, 2023).”
From A Scoping Review on How Generative Artificial Intelligence Transforms Assessment in Higher Education (2024) · cites this paper as Geerling et al., 2023
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How often do University Students use Artificial Intelligence in Their Studies?
Nechyporenko, V., Hordiienko, N., Pozdnіakova, O., Pozdniakova-Kyrbiatieva, E., & Siliavina, Y. (2025) · WSEAS Transactions on Information Science and Applications archive · doi:10.37394/23209.2025.22.18 · Cited by 5 · see citing works
Redefining Literature Education: The Role of ChatGPT in Undergraduate Courses
Bellot, A. R., Gutiérrez‐Colón, M., & Baran, K. A. (2025) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-025-00497-3 · Cited by 10 · see citing works
The Impact of Learner Content and User Interface Design on E-Learning Quality
Yeboah, A. N., & Goosen, L. (2023) · Advances in educational technologies and instructional design book series · doi:10.4018/978-1-6684-7583-6.ch006 · Cited by 5 · see citing works
Kosbar, Y. (2025) · World Futures Review · doi:10.1177/19467567251406292 · Cited by 7 · see citing works
Han, Y., Hou, J., & Sun, Y. (2023) · IEEE International Conference on Computing (ICOCO) · doi:10.1109/icoco59262.2023.10397642 · Cited by 4 · see citing works
Goorts, L., Hollevoet, R., Xia, V., Cammaerts, F., & Güngör, A. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100475 · Cited by 6 · see citing works
Zakhem, N. B., Diab, M. B., & Tahan, S. A. (2025) · Administrative Sciences · doi:10.3390/admsci15110412 · Cited by 5 · see citing works
Multilingual capabilities of GPT: A study of structural ambiguity
Yoo, M. H., Kim, J.-S., & Song, S. (2025) · PLoS ONE · doi:10.1371/journal.pone.0326943 · Cited by 5 · see citing works
Wang, S., Yan, R., & Hu, W. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13839-4 · Cited by 3 · see citing works
Large-scale testing in the face of AI
Volante, L., & DeLuca, C. (2024) · Assessment and Development Matters · doi:10.53841/bpsadm.2024.16.1.48 · Cited by 3 · see citing works
Singh, J. P., Mishra, N., & Mishra, K. K. (2024) · Advances in higher education and professional development book series · doi:10.4018/979-8-3693-6765-0.ch007 · Cited by 3 · see citing works
Shear, H. E., Britton, L. L., Schaefer, K. A., Thapa, B., & Bergtold, J. S. (2023) · Journal of the Agricultural and Applied Economics Association · doi:10.1002/jaa2.98 · Cited by 3 · see citing works
A Conversational Human-Computer Interface for Smart Energy System Simulation Environments
Dengler, G., Bazan, P., German, R., Lalbakhsh, P., & Liebman, A. (2023) · Winter Simulation Conference · doi:10.1109/wsc60868.2023.10408707 · Cited by 3 · see citing works
Malaquias, R., & Hwang, Y. (2025) · Journal of Management History · doi:10.1108/jmh-01-2025-0008 · Cited by 2 · see citing works
Research on Revolutionising Learning with AI-Powered Chatbots and NLP Techniques
Rathod, K. (2024) · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · doi:10.55041/ijsrem31253 · Cited by 2 · see citing works
Pham, T., Tran, M., Kavalchuk, I., & Nguyen, T. N. (2024) · Lecture notes in mechanical engineering · doi:10.1007/978-3-031-59042-9_18 · Cited by 2 · see citing works
ChatGPT in Academia and Research: A Comprehensive Review of Integrating AI in Higher Education
Thakkar, A., Asmelash, A., & Hirpo, H. D. (2025) · Springer eBooks · doi:10.1007/978-981-96-2355-6_10 · Cited by 2 · see citing works
Gpt-3.5 Hallucinates Nonexistent Citations: Evidence from EconomicsPreprint
Buchanan, J. A., & Shapoval, O. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4472220 · Cited by 2 · see citing works
Performance of Artificial Intelligence: Does Artificial Intelligence Dream of Electric SheepPreprint
Ioku, T., Kondo, S., & Watanabe, Y. (2024) · Research Square · doi:10.35542/osf.io/pgy5f · Cited by 2 · see citing works
A multidisciplinary perspective to teaching international trade
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Educational technology for teaching economics–Where to start and how to grow?
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Learning by experimenting: An introductory course on experimental economics
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Language as Data: A Survey of Natural Language Processing for Economics and Finance
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Not a Swiss Army Knife: Academics' Perceptions of Trade‐Offs around Generative AI Use
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Identifying the Rising Stars of Economics Education
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Self-citationScaling Open-Ended Survey Responses Using LLM-Paired Comparisons
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Ethical exploration of chatGPT in the modern K-14 economics classroom
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Expert Evaluation of Export Control-Related Question Answering Capabilities of LLMs
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Chat GPT in Higher Education: An Analysis of Research Trends and Themes
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USE OF ARTIFICIAL INTELLIGENCE IN HIGHER EDUCATION: STUDYING THE NEEDS OF UKRAINIAN STUDENTS
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Frenemies - Unleashing the Power of ChatGPT in Assessments
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The Use of Generative Artificial Intelligence in EconomicsPreprint
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Generative AI-Enhanced Oral Exams in Economics Education
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Editor-in-Chief Transition and Ancillaries of Undergraduate Economics Programs Symposium
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- Authors
- Geerling, W., Mateer, G. D., Wooten, J., & Damodaran, N.
- Affiliations
- The University of Texas at Austin (US)
- Virginia Tech (US)
- O. P. Jindal Global University (IN)
- Corresponding author
- Geerling, Wayne
- Venue
- The American Economist
- Publisher
- SAGE Publications
- Published
- 2023-04-08
- Volume
- 68
- Issue
- 2
- Pages
- 233-245
- ISSN
- 0569-4345 (ISSN-L)
- 2328-1235
- OpenAlex
- W4362723020
- Cited by
- 119
- References
- 34
- Citations by year
- 2026: 15 · 2025: 38 · 2024: 38 · 2023: 28
- FWCI
- 27.93
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- The American Economist (publishedVersion, cc-by)
- VTechWorks (Virginia Tech) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- ChatGPT, economics education, artificial intelligence in education, automated assessment, microeconomics, macroeconomics, economic principles, chatbots, experiential learning, curriculum redesign
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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